This invention relates generally to the processing of digital image data. More specifically, this invention relates to image data processing using digital techniques for image enhancement and enlargement.
Digital image processing has taken an increasing importance as a result of the technological advances in image acquisition and image communication; and can provide advantages over conventional analog image information handling, e.g., undisturbed access of the "raw data set", objective image evaluation, quantitative analysis of the image information, and reduced costs and increased flexibility of image data handling. While a complex infrastructure network is in place for high-speed image communication with local, national and international access, the general use of digital image processing is hindered through lack of universal standards for identifying image information. Unfortunately, visual image perception varies amongst individuals and depends strongly on the image perception and pattern recognition ability. This is the reason why the same image is evaluated quite differently by more than one person. Of course, the lack of consistency in image information analysis and display is extremely problematic and creates serious concerns for image evaluation.
There are many kinds of information contained in images, but only a few classes may be important in image communication, i.e., detail-oriented (scientific, technical and medical images), composition-oriented (arts, materials science) or information-oriented (binary and CAD, computer assisted drawing). Most important are detail containing images which describe two-or three-dimensional data sets dealing with spacial features. The lack of proper pixel accurate tools for objective description of image details as well as image imperfections produced by acquisition and transmission (noise) limit image communication at this time to information-oriented contents only.
When analyzing images (e.g., microscopic images derived from SEM or TEM; or medical imagery such as mammograms or x-rays), the image analysts are limited most by their own visual system (e.g., the human eye) regarding image perception and pattern recognition, since most modern imaging instruments (e.g., microscope, x-ray device, mammography device) provide more data than the eye can process. In general, such data are spacial information documented with certain contrast mechanisms and translated into images. Irrespective of the kind of imaging technique used, the imaging information must be communicated to the visual system for evaluation of its information content at the level of imaging instrument contrast resolution as well as spacial resolution. Since the visual perception is limited in intensity range (IR) to 100-200 intensity levels (7-8 bit in self illuminated images of a video monitor) and resolution to 500-1,000 pixels per picture width (PW), the imaging instruments full frame image information must often be compressed to fit within these values. Image evaluation is primarily a process of pattern recognition which works at a much lower information density than the eye can perceive. Only patterns of large image components of high contrast, high edge sharpness and a few intensity levels (4 bit) are recognized. All other small detail information can only be partially recognized and therefore is commonly generalized as image background or texture. However, in microscopy, radiology, x-ray and other imaging sciences, such background contains a wealth of information of acquired image detail data which is commonly lost in visual analog evaluation. Simply increasing the image magnification during acquisition will not fully access these details due to visual or instrumental limitations (low contrasts, sample or instrument instabilities, etc.). It is the ability of digital image processing to make these details visually accessible (detail enhancement) in already acquired images and to provide simple tools for their evaluation, quantitation and communication which makes such processing an important and essential tool for image evaluation. Unfortunately, as will be discussed below, presently available and known digital processing techniques fall far short of providing the required detail enhancements and are associated with serious drawbacks and deficiencies.
In single digital images, intensity fluctuations caused by random noise and small structural (spacial) image details of only a few pixels cannot be identified since no information other than the pixel's intensities are available in a single data set. However, digital image processing methods can be applied for separating image contents on account of certain intensity criteria. The separation of noise and detail structures can be defined by a threshold of intensity variations below which spacial variations are thought to represent noise and are eliminated (smoothed) but above which the intensity variations are defined as significant and are maintained. Conventional image processing methods apply spatially extended processing masks or Fourier filters for the determination of local intensity fluctuations (either in the space domain or in the Fourier domain) and use various methods for determination of the threshold intensity value. The utilized pixel area (mask) and an often used weighing factor applied to the local intensity distribution within the mask will shift the spacial boundary between (smoothed) background and (maintained) detail, altering the spacial dimensions of details. Using this technique, serious spacial artifacts are produced when structural features are similar in size or smaller than the effective spacial filter area. This problem occurs in all conventional processing modes using spacial kernels, Fourier filters or statistical approaches. In addition, certain significant spacial intensity distributions (significant structural patterns) may be seriously altered by eliminating or adding structures, indicating a strong dependency of the processing result on the image content. Such alteration of the spacial content of the original image (raw data set) is a serious limitation of all conventional noise filters in cases where the structural integrity of the image data is important--i.e., in structure characterization and quantitation. The extent of spacial artifacts in G7 conventional processing depends on the image content. Therefore, complex time-consuming determination of optimal processing parameters are required for each image in order to reduce processing artifacts. Also, conventional image processing speeds are so slow that visual control of intensity threshold adjustments and recognition of processing artifacts are significantly hindered. Therefore, conventional noise smoothing techniques are applied strictly to full frame images, thereby reducing the visualization and recognition of the produced artifacts.